controlnet-union-sdxl-1.0

thanks feiyuuu for report the problem. When using the default pose line the performance may be unstable, this is because the pose label use more thick line in training to have a better look. This difference can be fix by using the following method: Find the util.py in controlnetaux python package, usually the path is like: /your anaconda3 path/envs/your env name/lib/python3.8/site-packages/controlnetaux/openpose/util.py Replace the drawbodypose function with the following code: Use the code below to get started with the model. HumanArt [https://github.com/IDEA-Research/HumanArt], select 2000 images with ground truth pose annotations to generate images and calculate mAP. We are the SOTA…

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sd-turbo

Stability AI

SD-Turbo is a fast generative text-to-image model that can synthesize photorealistic images from a text prompt in a single network evaluation. We release SD-Turbo as a research artifact, and to study small, distilled text-to-image models. For increased quality and prompt understanding, we recommend SDXL-Turbo. Please note: For commercial use, please refer to https://stability.ai/license. SD-Turbo is a distilled version of Stable Diffusion 2.1, trained for real-time synthesis. SD-Turbo is based on a novel training method called Adversarial Diffusion Distillation (ADD) (see the technical report), which allows sampling large-scale foundational image diffusion models in 1 to 4 steps at high…

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LCM_Dreamshaper_v7

Simian Luo

Distilled from Dreamshaper v7 fine-tune of Stable-Diffusion v1-5 with only 4,000 training iterations (~32 A100 GPU Hours). By distilling classifier-free guidance into the model's input, LCM can generate high-quality images in very short inference time. We compare the inference time at the setting of 768 x 768 resolution, CFG scale w=8, batchsize=4, using a A800 GPU. You can try out Latency Consistency Models directly on: To run the model yourself, you can leverage the Diffusers library: 1. Install the library: 2. Run the model: For more information, please have a look at the official docs: https://huggingface.co/docs/diffusers/api/pipelines/latentconsistencymodels#latent-consistency-models…

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stable-diffusion-v1-5

SD v1.5

Modifications to the original model card are in red or green Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at 's Stable Diffusion blog. The Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. You can use this both with the Diffusers library and RunwayML GitHub repository ( now deprecated ), ComfyUI…

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dreamshaper-7

Lykon

lykon/dreamshaper-7 is a Stable Diffusion model that has been fine-tuned on runwayml/stable-diffusion-v1-5. For more general information on how to run text-to-image models with Diffusers, see the docs. - Version 8 focuses on improving what V7 started. Might be harder to do photorealism compared to realism focused models, as it might be hard to do anime compared to anime focused models, but it can do both pretty well if you're skilled enough. Check the examples! - Version 7 improves lora support, NSFW and realism. If you're interested in "absolute" realism, try AbsoluteReality. - Version 6 adds more lora support and more style in general. It should also be better at generating directly at…

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stable-diffusion-v1-4

CompVis

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at 's Stable Diffusion with Diffusers blog. The Stable-Diffusion-v1-4 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. This weights here are intended to be used with the Diffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase…